journal article·2023-07Open access

Experimental and computational study on the anti-rutting behavior of an asphalt mixture based on an advanced MTS test

Chuanyi Zhuang, Kun Chen, Yali Ye, Yan Hao, Jiahao Zhang

Abstract

The dynamic load of vehicles is the main cause of rutting on asphalt pavement. To study the magnitude of the rutting depth generated by the load frequency on asphalt pavement, the rutting test procedure was developed through a material test system (MTS). A rutting fixture for the MTS test system has been designed. Rutting tests were carried out on AC-13, AC-20, and SMA-13 modified asphalt mixtures at different temperatures and load frequencies. Track and detect rut deformation under different loading times. The finite element Abaqus was used for simulation analysis, and a paired sample t-test was performed between the finite element simulation results and the MTS rutting test results. Through multiple regression analysis of the MTS rutting test data, an indoor rutting prediction model for asphalt mixtures including load frequency parameters was established. The correlation between the rutting prediction model established in this study and the rutting prediction model Specifications for Design of Highway Asphalt Pavement (JTG D50-2017) reached 0.894. The researchers conducted the MTS rut loading test by adding AC-20 modified asphalt mixture. SPSS data analysis software was used to analyze the measured data and predicted data. The correlation coefficient between the predicted and measured values was 0.973. In the paired sample test, the significance probability P = 0.671 was much larger than 0.05, indicating that the data error of the two groups was small. It is verified that the rut prediction model established in this study has high accuracy.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Asphalt Pavement Performance Evaluation · Infrastructure Maintenance and Monitoring · Transport Systems and Technology

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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